摘要
针对网络流量序列的非线性和多时间尺度特性,提出了一种将小波变换与人工神经网络相结合进行网络流量预测的新模型。该模型吸取了小波变换的多分辨功能和人工神经网络的非线性逼近能力,对流量时间序列进行小波分解,得到小波变换尺度系数序列和小波系数序列,分别使用RBF神经网络和Elman神经网络进行预测,把两种预测的结果通过BP神经网络合成为最终预测结果。用实际网络流量对该模型进行验证,结果表明,该模型具有较高的预测效果。
Based on the multi-time scale and the nonlinear character of the network traffic time series, a new network traffic prediction model which combines the wavelet transform and neural network is presented. The suggested model has advantage with its absorbing some merits of wavelet transform and artificial neural network. First, the traffic time series are decomposed to the scaling coefficient series and wavelet coefficient series. Then, RBF neural network and Elman neural network are used respectively to make prediction. Finally, the two predictions are combined into the final result through BP neural network. The simulation results on real network traffic show the new model has better predictive precision.
出处
《计算机工程与设计》
CSCD
北大核心
2007年第21期5135-5136,5159,共3页
Computer Engineering and Design
基金
国防预研基金项目(A1420061266)
关键词
网络流量
小波变换
神经网络
结合
预测
network traffic
wavelet transform
neural network
combine
prediction
